code: def unlabeled_image_data_collate_fn(
      batch: List[Mapping[str, Any]], opts: argparse.Namespace
  ) -> Mapping[str, Any]:
      """
      Combines a list of dictionaries into a single dictionary by concatenating matching fields.
  
      Each input dictionary is expected to have items with `samples` and `sample_id` as keys. The value for
      `samples` is expected to be a tensor and the value for `sample_id` is expected to be an integer.
  
      This function adds `targets` field to the output dictionary with dummy values to meet the expectations of
      training engine.
  
      Args:
          batch: A list of dictionaries
          opts: An argparse.Namespace instance.
  
      Returns:
          A dictionary with `samples`, `sample_id` and `targets` as keys.
      """
      batch_size = len(batch)
      sample_size = [batch_size, *batch[0]["samples"].shape]
      img_dtype = batch[0]["samples"].dtype
  
      samples = torch.zeros(size=sample_size, dtype=img_dtype)
      sample_ids = torch.zeros(size=[batch_size], dtype=torch.long)
      for i, batch_i in enumerate(batch):
          samples[i] = batch_i["samples"]
          sample_ids[i] = batch_i["sample_id"]
  
      channels_last = getattr(opts, "common.channels_last")
      if channels_last:
          samples = samples.to(memory_format=torch.channels_last)
  
      # Add dummy labels to meet the expectations of training engine.
      dummy_labels = torch.full(
          size=[batch_size], fill_value=0, dtype=torch.long
      )
  
      return {
          "samples": samples,
          "sample_id": sample_ids,
          "targets": dummy_labels,
      }
  
file_path: ml-cvnets/data/collate_fns/collate_functions.py
imports: import argparse
  from typing import Any, List, Mapping
  import torch
  from torch import Tensor
  from torch.utils.data import default_collate
  from data.collate_fns import COLLATE_FN_REGISTRY
  from utils import logger
label_desc: Use image node_id 2 for calling a global function with example usage: unlabeled_image_data_collate_fn(batch, opts) and returns: dict